Cardiffnlp
Models by this creator
🛠️
twitter-roberta-base-sentiment-latest
436
The twitter-roberta-base-sentiment-latest model is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021 and fine-tuned for sentiment analysis using the TweetEval benchmark. This model builds on the original Twitter-based RoBERTa model and the TweetEval benchmark. The model is suitable for English language sentiment analysis and was created by the cardiffnlp team. Model inputs and outputs The twitter-roberta-base-sentiment-latest model takes in English text and outputs sentiment labels of 0 (Negative), 1 (Neutral), or 2 (Positive), along with confidence scores for each label. The model can be used for both simple sentiment analysis tasks as well as more advanced text classification projects. Inputs English text, such as tweets, reviews, or other short passages Outputs Sentiment label (0, 1, or 2) Confidence score for each sentiment label Capabilities The twitter-roberta-base-sentiment-latest model can accurately classify the sentiment of short English text. It excels at analyzing the emotional tone of tweets, social media posts, and other informal online content. The model was trained on a large, up-to-date dataset of tweets, giving it strong performance on the nuanced language used in many online conversations. What can I use it for? This sentiment analysis model can be used for a variety of applications, such as: Monitoring brand reputation and customer sentiment on social media Detecting emotional reactions to news, events, or products Analyzing customer feedback and reviews to inform business decisions Powering chatbots and virtual assistants with natural language understanding Things to try To get started with the twitter-roberta-base-sentiment-latest model, you can try experimenting with different types of text inputs, such as tweets, customer reviews, or news articles. See how the model performs on short, informal language versus more formal written content. You can also try combining this sentiment model with other NLP tasks, like topic modeling or named entity recognition, to gain deeper insights from your data.
Updated 5/28/2024
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twitter-roberta-base-sentiment
247
The twitter-roberta-base-sentiment model is a RoBERTa-base model trained on ~58M tweets and fine-tuned for sentiment analysis using the TweetEval benchmark. This model is suitable for analyzing the sentiment of English text, particularly tweets and other social media content. It can classify text as either negative, neutral, or positive. Compared to similar models like twitter-xlm-roberta-base-sentiment, which is a multilingual model, the twitter-roberta-base-sentiment is specialized for English. The sentiment-roberta-large-english model is another English-focused sentiment analysis model, but it is based on the larger RoBERTa-large architecture. Model inputs and outputs Inputs Text**: The model takes in English-language text, such as tweets, reviews, or other social media posts. Outputs Sentiment score**: The model outputs a sentiment score that classifies the input text as either negative (0), neutral (1), or positive (2). Capabilities The twitter-roberta-base-sentiment model can be used to perform reliable sentiment analysis on a variety of English-language text. It has been trained and evaluated on a wide range of datasets, including reviews, tweets, and other social media content, and has been shown to outperform models trained on a single dataset. What can I use it for? This model could be useful for a variety of applications that involve analyzing the sentiment of text, such as: Monitoring social media sentiment around a brand, product, or event Analyzing customer feedback and reviews to gain insights into customer satisfaction Identifying and tracking sentiment trends in online discussions or news coverage Things to try One interesting thing to try with this model is to compare its performance on different types of English-language text, such as formal writing versus informal social media posts. You could also experiment with using the model's output scores to track sentiment trends over time or to identify the most polarizing topics in a dataset.
Updated 5/28/2024
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twitter-xlm-roberta-base-sentiment
169
The twitter-xlm-roberta-base-sentiment model is a multilingual XLM-roBERTa-base model trained on ~198M tweets and fine-tuned for sentiment analysis. The model supports sentiment analysis in 8 languages (Arabic, English, French, German, Hindi, Italian, Spanish, and Portuguese), but can potentially be used for more languages as well. This model was developed by cardiffnlp. Similar models include the xlm-roberta-base-language-detection model, which is a fine-tuned version of the XLM-RoBERTa base model for language identification, and the xlm-roberta-large and xlm-roberta-base models, which are the base and large versions of the multilingual XLM-RoBERTa model. Model inputs and outputs Inputs Text sequences for sentiment analysis Outputs A label indicating the predicted sentiment (Positive, Negative, or Neutral) A score representing the confidence of the prediction Capabilities The twitter-xlm-roberta-base-sentiment model can perform sentiment analysis on text in 8 languages: Arabic, English, French, German, Hindi, Italian, Spanish, and Portuguese. It was trained on a large corpus of tweets, giving it the ability to analyze the sentiment of short, informal text. What can I use it for? This model can be used for a variety of applications that require multilingual sentiment analysis, such as social media monitoring, customer service analysis, and market research. By leveraging the model's ability to analyze sentiment in multiple languages, developers can build applications that can process text from a wide range of sources and users. Things to try One interesting thing to try with this model is to experiment with the different languages it supports. Since the model was trained on a diverse dataset of tweets, it may be able to capture nuances in sentiment that are specific to certain cultures or languages. Developers could try using the model to analyze sentiment in languages beyond the 8 it was specifically fine-tuned on, to see how it performs. Another idea is to compare the performance of this model to other sentiment analysis models, such as the bart-large-mnli or valhalla models, to see how it fares on different types of text and tasks.
Updated 5/28/2024
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tweet-topic-21-multi
62
The tweet-topic-21-multi model is a multilingual language model based on the TimeLMs architecture. It was trained on 124 million tweets from January 2018 to December 2021 and fine-tuned for multi-label topic classification on a corpus of 11,267 tweets. The model is suitable for English text and can classify tweets into 19 different topics, including arts & culture, business, celebrity & pop culture, diaries & daily life, family, fashion & style, film/TV & video, fitness & health, food & dining, gaming, learning & educational, music, news & social concern, relationships, science & technology, sports, travel & adventure, and youth & student life. Model inputs and outputs Inputs English text, such as tweets or short social media posts Outputs A list of topics that the input text is classified as, with each topic represented as a binary 0/1 value indicating whether the text belongs to that topic or not. Capabilities The tweet-topic-21-multi model is capable of accurately classifying short English text into multiple relevant topics. For example, the input text "It is great to see athletes promoting awareness for climate change." would be classified as belonging to the "news & social concern" and "sports" topics. What can I use it for? The tweet-topic-21-multi model can be used for a variety of applications, such as: Content Categorization**: Automatically organizing and indexing large collections of social media posts, news articles, or other short-form text based on their topical content. Trend Analysis**: Monitoring social media conversations to detect emerging trends and topics of interest. Personalization**: Tailoring content recommendations or marketing messages based on a user's predicted interests and preferences. Things to try One interesting aspect of the tweet-topic-21-multi model is its ability to handle multi-label classification. This means that a single input text can be assigned to multiple topics simultaneously, reflecting the diverse and overlapping nature of real-world content. Researchers and developers could explore how this capability can be leveraged to build more sophisticated text understanding and analysis applications.
Updated 5/28/2024
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twitter-roberta-base-emotion
42
The twitter-roberta-base-emotion model is an AI model developed by cardiffnlp that is trained on a large corpus of tweets for emotion classification. It is similar to other Twitter-focused language models like twitter-roberta-base-sentiment-latest and codebert-base, which are also built for specialized text analysis tasks. Model inputs and outputs The twitter-roberta-base-emotion model takes in text, typically in the form of tweets or other social media posts, and outputs an emotion classification. The specific emotions it can detect are not clearly defined in the provided information. Inputs Text, usually in the form of tweets or other social media posts Outputs Emotion classification, with labels like "Negative", "Neutral", and "Positive" Capabilities The twitter-roberta-base-emotion model is capable of analyzing the emotional content of text, particularly from social media sources. This can be useful for a variety of applications, such as sentiment analysis, customer service, and social media monitoring. What can I use it for? The twitter-roberta-base-emotion model can be used for any application that requires understanding the emotional tone of text, especially from social media platforms. This could include things like: Monitoring brand sentiment on social media Analyzing customer feedback and support conversations Detecting emotional patterns in online discussions Powering chatbots and other conversational AI systems Things to try Some ideas for using the twitter-roberta-base-emotion model include: Integrating it into a social media monitoring or customer service platform to automatically classify the emotional tone of incoming messages Experimenting with the model's performance on different types of text, such as longer-form content or specialized domains, to see how it generalizes Combining the emotion classification with other natural language processing tasks, like named entity recognition or topic modeling, to gain deeper insights into the text
Updated 9/6/2024